1 code implementation • NeurIPS 2023 • Kyowoon Lee, Seongun Kim, Jaesik Choi
We also illustrate that our approach presents explainability by presenting the attribution maps of the gap predictor and highlighting error-prone transitions, allowing for a deeper understanding of the generated plans.
1 code implementation • 30 Oct 2023 • Seongun Kim, Kyowoon Lee, Jaesik Choi
We validate the effectiveness of our approach on complex navigation and robotic manipulation tasks in terms of sample efficiency and state coverage speed.
no code implementations • 30 May 2019 • Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi
We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD.
1 code implementation • ICML 2018 • Kyowoon Lee, Sol-A Kim, Jaesik Choi, Seong-Whan Lee
Many real-world applications of reinforcement learning require an agent to select optimal actions from continuous spaces.